Three shallow standard methods are evaluated, namely, OC-SVM [1]}, iForest [2]} and ARIMA [3]}.
In addition, five recent DL-based methods have been considered: DA-GMM [4]}, THOC [5]} USAD [6]}, GDN [7]} and MTAD-GAT [8]}. The latter has been selected according to the following criteria: (1) Relevance of the topic: all the chosen anomaly detection algorithms are unsupervised and have been specifically designed for detecting anomalies in time-series. (2) Publication date: all the DL-based algorithms are recent. In particular, they have been introduced
between 2018 and early 2022. (3) Impact: the chosen algorithms have been published in top-tier conferences and are highly cited papers from the field. (4) Code availability: the official codes of the selected algorithms are publicly available. (5) Diversity: methods from different paradigms have been considered. The only paradigm that was ignored is the distance-based since we were not able to find a deep learning approach falling in this category.
